Exploratory Data Analysis

1.1: Load data

load("../analysis/data/.RData")
#load("../analysis/data/RData.dms") 
QBCrossSectional$Week <- as.factor(QBCrossSectional$Week)
attach(QBCrossSectional)

## The following object is masked from package:ggplot2:
## 
##     Position

1.2: Structures QBCrossSectional and QBPanels

1.2.1 QB (Quarterback)

head(QBCrossSectional)

## # A tibble: 6 x 21
##   PlayerID Week  Position Opponent TeamIsHome GameDate   PassingCompleti…
##      <dbl> <fct> <chr>    <chr>    <lgl>      <date>                <dbl>
## 1     6739 13    QB       NYJ      FALSE      2017-12-03               19
## 2     4314 3     QB       HOU      TRUE       2017-09-24               25
## 3    13320 10    QB       MIA      TRUE       2017-11-13               21
## 4    18857 5     QB       KC       TRUE       2017-10-08               16
## 5    14536 8     QB       HOU      TRUE       2017-10-29               26
## 6    18857 4     QB       TEN      TRUE       2017-10-01               25
## # … with 14 more variables: PassingAttempts <dbl>,
## #   PassingCompletionPercentage <dbl>, PassingYards <dbl>,
## #   PassingYardsPerAttempt <dbl>, PassingTouchdowns <dbl>,
## #   PassingInterceptions <dbl>, PassingRating <dbl>,
## #   RushingAttempts <dbl>, RushingYards <dbl>,
## #   RushingYardsPerAttempt <dbl>, RushingTouchdowns <dbl>,
## #   FumblesLost <dbl>, FantasyPoints <dbl>, Team <chr>

summary(QBCrossSectional)

##     PlayerID          Week       Position           Opponent        
##  Min.   :  611   2      : 30   Length:453         Length:453        
##  1st Qu.: 7242   13     : 29   Class :character   Class :character  
##  Median :13723   14     : 29   Mode  :character   Mode  :character  
##  Mean   :11932   3      : 28                                        
##  3rd Qu.:16763   4      : 28                                        
##  Max.   :19029   12     : 28                                        
##                  (Other):281                                        
##  TeamIsHome         GameDate          PassingCompletions PassingAttempts
##  Mode :logical   Min.   :2017-09-07   Min.   : 5.00      Min.   :10.00  
##  FALSE:226       1st Qu.:2017-10-08   1st Qu.:18.00      1st Qu.:29.00  
##  TRUE :227       Median :2017-11-05   Median :21.00      Median :33.00  
##                  Mean   :2017-11-05   Mean   :21.24      Mean   :33.62  
##                  3rd Qu.:2017-12-03   3rd Qu.:25.00      3rd Qu.:38.00  
##                  Max.   :2017-12-31   Max.   :44.00      Max.   :66.00  
##                                                                         
##  PassingCompletionPercentage  PassingYards   PassingYardsPerAttempt
##  Min.   :38.70               Min.   : 57.0   Min.   : 3.100        
##  1st Qu.:57.10               1st Qu.:199.0   1st Qu.: 6.200        
##  Median :63.20               Median :241.0   Median : 7.200        
##  Mean   :63.48               Mean   :244.6   Mean   : 7.378        
##  3rd Qu.:69.40               3rd Qu.:291.0   3rd Qu.: 8.400        
##  Max.   :87.00               Max.   :506.0   Max.   :14.100        
##                                                                    
##  PassingTouchdowns PassingInterceptions PassingRating    RushingAttempts 
##  Min.   :0.000     Min.   :0.00         Min.   : 31.14   Min.   : 0.000  
##  1st Qu.:1.000     1st Qu.:0.00         1st Qu.: 77.92   1st Qu.: 1.000  
##  Median :1.000     Median :0.00         Median : 92.94   Median : 3.000  
##  Mean   :1.587     Mean   :0.66         Mean   : 93.88   Mean   : 3.185  
##  3rd Qu.:2.000     3rd Qu.:1.00         3rd Qu.:109.84   3rd Qu.: 5.000  
##  Max.   :5.000     Max.   :4.00         Max.   :150.69   Max.   :14.000  
##                                                                          
##   RushingYards   RushingYardsPerAttempt RushingTouchdowns  FumblesLost    
##  Min.   :-8.00   Min.   :-2.700         Min.   :0.0000    Min.   :0.0000  
##  1st Qu.: 0.00   1st Qu.: 0.000         1st Qu.:0.0000    1st Qu.:0.0000  
##  Median : 8.00   Median : 3.000         Median :0.0000    Median :0.0000  
##  Mean   :14.38   Mean   : 3.839         Mean   :0.1457    Mean   :0.1766  
##  3rd Qu.:23.00   3rd Qu.: 6.000         3rd Qu.:0.0000    3rd Qu.:0.0000  
##  Max.   :95.00   Max.   :70.000         Max.   :2.0000    Max.   :3.0000  
##                                                                           
##  FantasyPoints       Team          
##  Min.   : 7.12   Length:453        
##  1st Qu.:11.86   Class :character  
##  Median :15.86   Mode  :character  
##  Mean   :16.89                     
##  3rd Qu.:20.68                     
##  Max.   :37.64                     
## 

1.2.2 QB (Panel Data)

str(QBPanels)

## List of 17
##  $ Opponent                   :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : chr [1:56] NA "CHI" "DET" "SEA" ...
##   ..$ 2       : chr [1:56] "CLE" "GB" "IND" "ATL" ...
##   ..$ 3       : chr [1:56] NA "DET" "DAL" "CIN" ...
##   ..$ 4       : chr [1:56] "PIT" "BUF" "SF" "CHI" ...
##   ..$ 5       : chr [1:56] "OAK" NA "PHI" "DAL" ...
##   ..$ 6       : chr [1:56] NA "MIA" "TB" NA ...
##   ..$ 7       : chr [1:56] "MIN" "NE" NA NA ...
##   ..$ 8       : chr [1:56] "MIA" "NYJ" NA NA ...
##   ..$ 9       : chr [1:56] "TEN" "CAR" NA NA ...
##   ..$ 10      : chr [1:56] NA "DAL" NA NA ...
##   ..$ 11      : chr [1:56] "GB" "SEA" NA NA ...
##   ..$ 12      : chr [1:56] "HOU" "TB" NA NA ...
##   ..$ 13      : chr [1:56] "DET" NA NA NA ...
##   ..$ 14      : chr [1:56] "PIT" NA NA NA ...
##   ..$ 15      : chr [1:56] "CLE" "TB" NA "CAR" ...
##   ..$ 16      : chr [1:56] "IND" "NO" NA NA ...
##   ..$ 17      : chr [1:56] "CIN" "CAR" NA NA ...
##  $ TeamIsHome                 :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : logi [1:56] NA FALSE FALSE TRUE FALSE TRUE ...
##   ..$ 2       : logi [1:56] TRUE TRUE FALSE FALSE TRUE FALSE ...
##   ..$ 3       : logi [1:56] NA FALSE TRUE TRUE FALSE TRUE ...
##   ..$ 4       : logi [1:56] TRUE TRUE TRUE TRUE FALSE TRUE ...
##   ..$ 5       : logi [1:56] FALSE NA FALSE FALSE NA FALSE ...
##   ..$ 6       : logi [1:56] NA TRUE TRUE NA FALSE FALSE ...
##   ..$ 7       : logi [1:56] FALSE FALSE NA NA TRUE TRUE ...
##   ..$ 8       : logi [1:56] TRUE FALSE NA NA FALSE TRUE ...
##   ..$ 9       : logi [1:56] FALSE FALSE NA NA NA NA ...
##   ..$ 10      : logi [1:56] NA TRUE NA NA FALSE FALSE ...
##   ..$ 11      : logi [1:56] FALSE FALSE NA NA TRUE FALSE ...
##   ..$ 12      : logi [1:56] TRUE TRUE NA NA TRUE TRUE ...
##   ..$ 13      : logi [1:56] TRUE NA NA NA FALSE FALSE ...
##   ..$ 14      : logi [1:56] FALSE NA NA NA TRUE FALSE ...
##   ..$ 15      : logi [1:56] FALSE FALSE NA FALSE TRUE FALSE ...
##   ..$ 16      : logi [1:56] TRUE FALSE NA NA FALSE TRUE ...
##   ..$ 17      : logi [1:56] TRUE TRUE NA NA NA TRUE ...
##  $ PassingCompletions         :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 21 27 28 24 16 NA NA 26 NA ...
##   ..$ 2       : num [1:56] 25 19 19 33 23 30 NA 22 16 NA ...
##   ..$ 3       : num [1:56] NA 24 29 28 22 25 NA 35 18 NA ...
##   ..$ 4       : num [1:56] 31 24 33 18 18 32 NA 30 NA NA ...
##   ..$ 5       : num [1:56] 19 NA 28 19 NA 30 NA 21 23 NA ...
##   ..$ 6       : num [1:56] NA 24 18 NA 17 20 NA 11 31 NA ...
##   ..$ 7       : num [1:56] 27 23 NA NA 14 21 NA 19 17 13 ...
##   ..$ 8       : num [1:56] 10 18 NA NA 17 32 NA NA 26 NA ...
##   ..$ 9       : num [1:56] 34 24 NA NA NA NA 15 20 14 NA ...
##   ..$ 10      : num [1:56] NA 22 NA NA 19 25 24 28 23 NA ...
##   ..$ 11      : num [1:56] 22 19 NA NA 30 30 NA 19 NA 17 ...
##   ..$ 12      : num [1:56] 20 26 NA NA 33 18 NA NA 19 23 ...
##   ..$ 13      : num [1:56] 23 NA NA NA 24 21 NA NA 26 NA ...
##   ..$ 14      : num [1:56] 20 NA NA NA 44 24 NA 31 NA NA ...
##   ..$ 15      : num [1:56] 26 17 NA 26 22 22 NA 37 NA NA ...
##   ..$ 16      : num [1:56] 29 22 NA NA 20 21 20 NA NA NA ...
##   ..$ 17      : num [1:56] 25 28 NA NA NA 18 15 NA NA NA ...
##  $ PassingAttempts            :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 30 48 42 36 36 NA NA 39 NA ...
##   ..$ 2       : num [1:56] 34 28 36 50 35 39 NA 32 24 NA ...
##   ..$ 3       : num [1:56] NA 35 48 42 39 35 NA 47 23 NA ...
##   ..$ 4       : num [1:56] 49 42 51 26 30 45 NA 49 NA NA ...
##   ..$ 5       : num [1:56] 26 NA 44 29 NA 40 NA 36 30 NA ...
##   ..$ 6       : num [1:56] NA 35 22 NA 25 38 NA 19 47 NA ...
##   ..$ 7       : num [1:56] 39 33 NA NA 24 29 NA 39 27 21 ...
##   ..$ 8       : num [1:56] 15 29 NA NA 31 47 NA NA 33 NA ...
##   ..$ 9       : num [1:56] 52 38 NA NA NA NA 30 36 20 NA ...
##   ..$ 10      : num [1:56] NA 29 NA NA 31 34 47 37 39 NA ...
##   ..$ 11      : num [1:56] 28 27 NA NA 45 37 NA 35 NA 28 ...
##   ..$ 12      : num [1:56] 32 35 NA NA 45 28 NA NA 36 34 ...
##   ..$ 13      : num [1:56] 36 NA NA NA 40 30 NA NA 36 NA ...
##   ..$ 14      : num [1:56] 35 NA NA NA 66 43 NA 46 NA NA ...
##   ..$ 15      : num [1:56] 42 31 NA 45 30 35 NA 57 NA NA ...
##   ..$ 16      : num [1:56] 38 36 NA NA 29 28 34 NA NA NA ...
##   ..$ 17      : num [1:56] 47 45 NA NA NA 37 34 NA NA NA ...
##  $ PassingCompletionPercentage:Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 70 56.2 66.7 66.7 44.4 NA NA 66.7 NA ...
##   ..$ 2       : num [1:56] 73.5 67.9 52.8 66 65.7 76.9 NA 68.8 66.7 NA ...
##   ..$ 3       : num [1:56] NA 68.6 60.4 66.7 56.4 71.4 NA 74.5 78.3 NA ...
##   ..$ 4       : num [1:56] 63.3 57.1 64.7 69.2 60 71.1 NA 61.2 NA NA ...
##   ..$ 5       : num [1:56] 73.1 NA 63.6 65.5 NA 75 NA 58.3 76.7 NA ...
##   ..$ 6       : num [1:56] NA 68.6 81.8 NA 68 52.6 NA 57.9 66 NA ...
##   ..$ 7       : num [1:56] 69.2 69.7 NA NA 58.3 72.4 NA 48.7 63 61.9 ...
##   ..$ 8       : num [1:56] 66.7 62.1 NA NA 54.8 68.1 NA NA 78.8 NA ...
##   ..$ 9       : num [1:56] 65.4 63.2 NA NA NA NA 50 55.6 70 NA ...
##   ..$ 10      : num [1:56] NA 75.9 NA NA 61.3 73.5 51.1 75.7 59 NA ...
##   ..$ 11      : num [1:56] 78.6 70.4 NA NA 66.7 81.1 NA 54.3 NA 60.7 ...
##   ..$ 12      : num [1:56] 62.5 74.3 NA NA 73.3 64.3 NA NA 52.8 67.6 ...
##   ..$ 13      : num [1:56] 63.9 NA NA NA 60 70 NA NA 72.2 NA ...
##   ..$ 14      : num [1:56] 57.1 NA NA NA 66.7 55.8 NA 67.4 NA NA ...
##   ..$ 15      : num [1:56] 61.9 54.8 NA 57.8 73.3 62.9 NA 64.9 NA NA ...
##   ..$ 16      : num [1:56] 76.3 61.1 NA NA 69 75 58.8 NA NA NA ...
##   ..$ 17      : num [1:56] 53.2 62.2 NA NA NA 48.6 44.1 NA NA NA ...
##  $ PassingYards               :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 321 268 311 263 267 NA NA 187 NA ...
##   ..$ 2       : num [1:56] 217 252 332 343 243 447 NA 239 166 NA ...
##   ..$ 3       : num [1:56] NA 294 325 313 235 378 NA 366 249 NA ...
##   ..$ 4       : num [1:56] 235 242 357 179 216 307 NA 288 NA NA ...
##   ..$ 5       : num [1:56] 222 NA 291 221 NA 303 NA 225 194 NA ...
##   ..$ 6       : num [1:56] NA 248 283 NA 252 257 NA 128 354 NA ...
##   ..$ 7       : num [1:56] 186 233 NA NA 224 249 NA 134 209 188 ...
##   ..$ 8       : num [1:56] 101 254 NA NA 317 333 NA NA 257 NA ...
##   ..$ 9       : num [1:56] 261 313 NA NA NA NA 201 220 140 NA ...
##   ..$ 10      : num [1:56] NA 215 NA NA 236 266 273 273 262 NA ...
##   ..$ 11      : num [1:56] 183 195 NA NA 299 340 NA 205 NA 282 ...
##   ..$ 12      : num [1:56] 141 317 NA NA 351 227 NA NA 307 215 ...
##   ..$ 13      : num [1:56] 269 NA NA NA 290 258 NA NA 331 NA ...
##   ..$ 14      : num [1:56] 269 NA NA NA 506 233 NA 228 NA NA ...
##   ..$ 15      : num [1:56] 288 212 NA 290 281 298 NA 434 NA NA ...
##   ..$ 16      : num [1:56] 237 288 NA NA 226 224 209 NA NA NA ...
##   ..$ 17      : num [1:56] 203 317 NA NA NA 190 145 NA NA NA ...
##  $ PassingYardsPerAttempt     :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 10.7 5.6 7.4 7.3 7.4 NA NA 4.8 NA ...
##   ..$ 2       : num [1:56] 6.4 9 9.2 6.9 6.9 11.5 NA 7.5 6.9 NA ...
##   ..$ 3       : num [1:56] NA 8.4 6.8 7.5 6 10.8 NA 7.8 10.8 NA ...
##   ..$ 4       : num [1:56] 4.8 5.8 7 6.9 7.2 6.8 NA 5.9 NA NA ...
##   ..$ 5       : num [1:56] 8.5 NA 6.6 7.6 NA 7.6 NA 6.2 6.5 NA ...
##   ..$ 6       : num [1:56] NA 7.1 12.9 NA 10.1 6.8 NA 6.7 7.5 NA ...
##   ..$ 7       : num [1:56] 4.8 7.1 NA NA 9.3 8.6 NA 3.4 7.7 9 ...
##   ..$ 8       : num [1:56] 6.7 8.8 NA NA 10.2 7.1 NA NA 7.8 NA ...
##   ..$ 9       : num [1:56] 5 8.2 NA NA NA NA 6.7 6.1 7 NA ...
##   ..$ 10      : num [1:56] NA 7.4 NA NA 7.6 7.8 5.8 7.4 6.7 NA ...
##   ..$ 11      : num [1:56] 6.5 7.2 NA NA 6.6 9.2 NA 5.9 NA 10.1 ...
##   ..$ 12      : num [1:56] 4.4 9.1 NA NA 7.8 8.1 NA NA 8.5 6.3 ...
##   ..$ 13      : num [1:56] 7.5 NA NA NA 7.2 8.6 NA NA 9.2 NA ...
##   ..$ 14      : num [1:56] 7.7 NA NA NA 7.7 5.4 NA 5 NA NA ...
##   ..$ 15      : num [1:56] 6.9 6.8 NA 6.4 9.4 8.5 NA 7.6 NA NA ...
##   ..$ 16      : num [1:56] 6.2 8 NA NA 7.8 8 6.1 NA NA NA ...
##   ..$ 17      : num [1:56] 4.3 7 NA NA NA 5.1 4.3 NA NA NA ...
##  $ PassingTouchdowns          :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 1 1 1 2 0 NA NA 0 NA ...
##   ..$ 2       : num [1:56] 2 1 1 2 2 3 NA 1 2 NA ...
##   ..$ 3       : num [1:56] NA 2 2 3 1 5 NA 3 1 NA ...
##   ..$ 4       : num [1:56] 1 1 1 4 1 2 NA 2 NA NA ...
##   ..$ 5       : num [1:56] 0 NA 1 3 NA 1 NA 2 2 NA ...
##   ..$ 6       : num [1:56] NA 1 3 NA 1 2 NA 1 2 NA ...
##   ..$ 7       : num [1:56] 1 1 NA NA 2 2 NA 1 3 2 ...
##   ..$ 8       : num [1:56] 1 2 NA NA 1 1 NA NA 2 NA ...
##   ..$ 9       : num [1:56] 2 2 NA NA NA NA 2 2 1 NA ...
##   ..$ 10      : num [1:56] NA 2 NA NA 2 3 1 2 1 NA ...
##   ..$ 11      : num [1:56] 1 2 NA NA 4 3 NA 0 NA 1 ...
##   ..$ 12      : num [1:56] 0 1 NA NA 4 4 NA NA 3 1 ...
##   ..$ 13      : num [1:56] 2 NA NA NA 2 0 NA NA 1 NA ...
##   ..$ 14      : num [1:56] 2 NA NA NA 2 1 NA 1 NA NA ...
##   ..$ 15      : num [1:56] 1 1 NA 3 2 1 NA 3 NA NA ...
##   ..$ 16      : num [1:56] 2 1 NA NA 2 2 2 NA NA NA ...
##   ..$ 17      : num [1:56] 2 1 NA NA NA 2 1 NA NA NA ...
##  $ PassingInterceptions       :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 0 3 1 1 0 NA NA 2 NA ...
##   ..$ 2       : num [1:56] 1 0 1 1 0 0 NA 1 0 NA ...
##   ..$ 3       : num [1:56] NA 3 0 1 0 0 NA 2 0 NA ...
##   ..$ 4       : num [1:56] 2 2 1 0 1 0 NA 0 NA NA ...
##   ..$ 5       : num [1:56] 0 NA 0 0 NA 1 NA 1 1 NA ...
##   ..$ 6       : num [1:56] NA 1 1 NA 1 1 NA 0 2 NA ...
##   ..$ 7       : num [1:56] 0 0 NA NA 0 0 NA 0 1 1 ...
##   ..$ 8       : num [1:56] 0 0 NA NA 1 0 NA NA 0 NA ...
##   ..$ 9       : num [1:56] 2 1 NA NA NA NA 1 1 0 NA ...
##   ..$ 10      : num [1:56] NA 1 NA NA 1 0 0 0 1 NA ...
##   ..$ 11      : num [1:56] 1 0 NA NA 0 0 NA 0 NA 0 ...
##   ..$ 12      : num [1:56] 0 0 NA NA 2 1 NA NA 0 2 ...
##   ..$ 13      : num [1:56] 0 NA NA NA 1 1 NA NA 0 NA ...
##   ..$ 14      : num [1:56] 1 NA NA NA 0 2 NA 2 NA NA ...
##   ..$ 15      : num [1:56] 0 0 NA 3 1 1 NA 1 NA NA ...
##   ..$ 16      : num [1:56] 0 1 NA NA 0 1 2 NA NA NA ...
##   ..$ 17      : num [1:56] 1 0 NA NA NA 0 1 NA NA NA ...
##  $ PassingRating              :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 116.1 53.1 86.5 95 ...
##   ..$ 2       : num [1:56] 97.3 108 82.2 90.7 104.8 ...
##   ..$ 3       : num [1:56] NA 77.6 94.5 102.6 82.8 ...
##   ..$ 4       : num [1:56] 64.6 61.8 83.5 128 79.3 ...
##   ..$ 5       : num [1:56] 98.6 NA 90.2 122.9 NA ...
##   ..$ 6       : num [1:56] NA 86.4 139.4 NA 97.4 ...
##   ..$ 7       : num [1:56] 88.2 99.7 NA NA 117.4 ...
##   ..$ 8       : num [1:56] 107.9 113.3 NA NA 87.7 ...
##   ..$ 9       : num [1:56] 74.3 95.6 NA NA NA ...
##   ..$ 10      : num [1:56] NA 104.8 NA NA 92.9 ...
##   ..$ 11      : num [1:56] 90.9 115.5 NA NA 115 ...
##   ..$ 12      : num [1:56] 72.5 111.2 NA NA 106.8 ...
##   ..$ 13      : num [1:56] 105 NA NA NA 88.5 ...
##   ..$ 14      : num [1:56] 88.9 NA NA NA 99.7 ...
##   ..$ 15      : num [1:56] 90.2 87 NA 71.5 110.6 ...
##   ..$ 16      : num [1:56] 109 84 NA NA 115 ...
##   ..$ 17      : num [1:56] 69.7 90.7 NA NA NA ...
##  $ RushingAttempts            :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 3 1 7 3 2 NA NA 2 NA ...
##   ..$ 2       : num [1:56] 1 1 3 2 2 2 NA 0 4 NA ...
##   ..$ 3       : num [1:56] NA 1 3 4 1 1 NA 0 4 NA ...
##   ..$ 4       : num [1:56] 0 1 2 1 1 1 NA 3 NA NA ...
##   ..$ 5       : num [1:56] 3 NA 0 4 NA 2 NA 0 3 NA ...
##   ..$ 6       : num [1:56] NA 0 5 NA 2 1 NA 1 3 NA ...
##   ..$ 7       : num [1:56] 1 3 NA NA 3 5 NA 0 3 0 ...
##   ..$ 8       : num [1:56] 1 6 NA NA 3 1 NA NA 1 NA ...
##   ..$ 9       : num [1:56] 1 1 NA NA NA NA 4 0 5 NA ...
##   ..$ 10      : num [1:56] NA 0 NA NA 2 1 1 0 0 NA ...
##   ..$ 11      : num [1:56] 2 3 NA NA 1 0 NA 2 NA 1 ...
##   ..$ 12      : num [1:56] 6 1 NA NA 3 5 NA NA 2 2 ...
##   ..$ 13      : num [1:56] 1 NA NA NA 1 0 NA NA 7 NA ...
##   ..$ 14      : num [1:56] 0 NA NA NA 1 0 NA 0 NA NA ...
##   ..$ 15      : num [1:56] 4 3 NA 6 3 2 NA 0 NA NA ...
##   ..$ 16      : num [1:56] 4 2 NA NA 1 2 2 NA NA NA ...
##   ..$ 17      : num [1:56] 0 3 NA NA NA 0 1 NA NA NA ...
##  $ RushingYards               :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 11 2 21 -8 0 NA NA 0 NA ...
##   ..$ 2       : num [1:56] -1 8 6 8 -2 9 NA 0 31 NA ...
##   ..$ 3       : num [1:56] NA -1 7 23 6 6 NA 0 7 NA ...
##   ..$ 4       : num [1:56] 0 8 1 -1 -1 2 NA 22 NA NA ...
##   ..$ 5       : num [1:56] -4 NA 0 32 NA 5 NA 0 -3 NA ...
##   ..$ 6       : num [1:56] NA 0 -4 NA -2 -1 NA 0 21 NA ...
##   ..$ 7       : num [1:56] 0 37 NA NA -3 5 NA 0 1 0 ...
##   ..$ 8       : num [1:56] 9 17 NA NA -2 2 NA NA 0 NA ...
##   ..$ 9       : num [1:56] 7 -1 NA NA NA NA 2 0 13 NA ...
##   ..$ 10      : num [1:56] NA 0 NA NA 5 0 1 0 0 NA ...
##   ..$ 11      : num [1:56] -1 12 NA NA 10 0 NA 5 NA 0 ...
##   ..$ 12      : num [1:56] 42 1 NA NA 25 -4 NA NA 28 9 ...
##   ..$ 13      : num [1:56] -1 NA NA NA 4 0 NA NA 19 NA ...
##   ..$ 14      : num [1:56] 0 NA NA NA 5 0 NA 0 NA NA ...
##   ..$ 15      : num [1:56] 3 29 NA 43 10 -2 NA 0 NA NA ...
##   ..$ 16      : num [1:56] -2 7 NA NA -1 6 1 NA NA NA ...
##   ..$ 17      : num [1:56] 0 14 NA NA NA 0 -1 NA NA NA ...
##  $ RushingYardsPerAttempt     :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 3.7 2 3 -2.7 0 NA NA 0 NA ...
##   ..$ 2       : num [1:56] -1 8 2 4 -1 4.5 NA 0 7.8 NA ...
##   ..$ 3       : num [1:56] NA -1 2.3 5.8 6 6 NA 0 1.8 NA ...
##   ..$ 4       : num [1:56] 0 8 0.5 -1 -1 2 NA 7.3 NA NA ...
##   ..$ 5       : num [1:56] -1.3 NA 0 8 NA 2.5 NA 0 -1 NA ...
##   ..$ 6       : num [1:56] NA 0 -0.8 NA -1 -1 NA 0 7 NA ...
##   ..$ 7       : num [1:56] 0 12.3 NA NA -1 1 NA 0 0.3 0 ...
##   ..$ 8       : num [1:56] 9 2.8 NA NA -0.7 2 NA NA 0 NA ...
##   ..$ 9       : num [1:56] 7 -1 NA NA NA NA 0.5 0 2.6 NA ...
##   ..$ 10      : num [1:56] NA 0 NA NA 2.5 0 1 0 0 NA ...
##   ..$ 11      : num [1:56] -0.5 4 NA NA 10 0 NA 2.5 NA 0 ...
##   ..$ 12      : num [1:56] 7 1 NA NA 8.3 -0.8 NA NA 14 4.5 ...
##   ..$ 13      : num [1:56] -1 NA NA NA 4 0 NA NA 2.7 NA ...
##   ..$ 14      : num [1:56] 0 NA NA NA 5 0 NA 0 NA NA ...
##   ..$ 15      : num [1:56] 0.8 9.7 NA 7.2 3.3 -1 NA 0 NA NA ...
##   ..$ 16      : num [1:56] -0.5 3.5 NA NA -1 3 0.5 NA NA NA ...
##   ..$ 17      : num [1:56] 0 4.7 NA NA NA 0 -1 NA NA NA ...
##  $ RushingTouchdowns          :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 0 0 0 0 0 NA NA 1 NA ...
##   ..$ 2       : num [1:56] 0 0 0 0 0 0 NA 0 0 NA ...
##   ..$ 3       : num [1:56] NA 0 0 0 0 0 NA 0 0 NA ...
##   ..$ 4       : num [1:56] 0 0 0 0 0 0 NA 1 NA NA ...
##   ..$ 5       : num [1:56] 0 NA 0 0 NA 0 NA 0 0 NA ...
##   ..$ 6       : num [1:56] NA 0 0 NA 0 0 NA 0 0 NA ...
##   ..$ 7       : num [1:56] 0 0 NA NA 0 0 NA 0 1 0 ...
##   ..$ 8       : num [1:56] 0 0 NA NA 0 0 NA NA 0 NA ...
##   ..$ 9       : num [1:56] 0 0 NA NA NA NA 0 0 1 NA ...
##   ..$ 10      : num [1:56] NA 0 NA NA 0 0 0 0 0 NA ...
##   ..$ 11      : num [1:56] 0 0 NA NA 0 0 NA 0 NA 0 ...
##   ..$ 12      : num [1:56] 0 0 NA NA 0 0 NA NA 0 0 ...
##   ..$ 13      : num [1:56] 0 NA NA NA 0 0 NA NA 2 NA ...
##   ..$ 14      : num [1:56] 0 NA NA NA 0 0 NA 0 NA NA ...
##   ..$ 15      : num [1:56] 1 0 NA 0 0 0 NA 0 NA NA ...
##   ..$ 16      : num [1:56] 0 0 NA NA 0 0 0 NA NA NA ...
##   ..$ 17      : num [1:56] 0 0 NA NA NA 0 0 NA NA NA ...
##  $ FumblesLost                :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 0 0 0 0 0 NA NA 0 NA ...
##   ..$ 2       : num [1:56] 0 0 0 1 0 0 NA 0 1 NA ...
##   ..$ 3       : num [1:56] NA 0 0 0 1 1 NA 0 0 NA ...
##   ..$ 4       : num [1:56] 0 1 0 0 0 0 NA 0 NA NA ...
##   ..$ 5       : num [1:56] 0 NA 0 0 NA 1 NA 1 0 NA ...
##   ..$ 6       : num [1:56] NA 0 0 NA 0 0 NA 0 0 NA ...
##   ..$ 7       : num [1:56] 0 0 NA NA 0 0 NA 1 0 0 ...
##   ..$ 8       : num [1:56] 0 2 NA NA 0 0 NA NA 0 NA ...
##   ..$ 9       : num [1:56] 0 0 NA NA NA NA 0 1 0 NA ...
##   ..$ 10      : num [1:56] NA 0 NA NA 0 0 0 1 0 NA ...
##   ..$ 11      : num [1:56] 0 0 NA NA 0 0 NA 0 NA 0 ...
##   ..$ 12      : num [1:56] 0 0 NA NA 0 1 NA NA 1 0 ...
##   ..$ 13      : num [1:56] 0 NA NA NA 0 0 NA NA 0 NA ...
##   ..$ 14      : num [1:56] 0 NA NA NA 0 0 NA 0 NA NA ...
##   ..$ 15      : num [1:56] 0 0 NA 0 0 0 NA 0 NA NA ...
##   ..$ 16      : num [1:56] 0 0 NA NA 0 0 0 NA NA NA ...
##   ..$ 17      : num [1:56] 0 0 NA NA NA 0 0 NA NA NA ...
##  $ FantasyPoints              :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : num [1:56] NA 17.94 8.92 16.54 15.72 ...
##   ..$ 2       : num [1:56] 14.6 14.9 15.9 18.5 17.5 ...
##   ..$ 3       : num [1:56] NA 13.7 21.7 24.8 12 ...
##   ..$ 4       : num [1:56] 9.4 8.48 16.38 23.06 10.54 ...
##   ..$ 5       : num [1:56] 8.48 NA 15.64 24.04 NA ...
##   ..$ 6       : num [1:56] NA 11.9 20.9 NA 11.9 ...
##   ..$ 7       : num [1:56] 11.4 17 NA NA 16.7 ...
##   ..$ 8       : num [1:56] 8.94 15.86 NA NA 14.48 ...
##   ..$ 9       : num [1:56] 15.1 18.4 NA NA NA ...
##   ..$ 10      : num [1:56] NA 14.6 NA NA 17.9 ...
##   ..$ 11      : num [1:56] 9.22 17 NA NA 28.96 ...
##   ..$ 12      : num [1:56] 9.84 16.78 NA NA 30.54 ...
##   ..$ 13      : num [1:56] 18.7 NA NA NA 18 ...
##   ..$ 14      : num [1:56] 16.8 NA NA NA 28.7 ...
##   ..$ 15      : num [1:56] 21.8 15.4 NA 21.9 18.2 ...
##   ..$ 16      : num [1:56] 17.3 14.2 NA NA 16.9 ...
##   ..$ 17      : num [1:56] 14.1 18.1 NA NA NA ...
##  $ Team                       :Classes 'tbl_df', 'tbl' and 'data.frame': 56 obs. of  18 variables:
##   ..$ PlayerID: num [1:56] 611 732 2428 2593 3807 ...
##   ..$ 1       : chr [1:56] NA "ATL" "ARI" "GB" ...
##   ..$ 2       : chr [1:56] "BAL" "ATL" "ARI" "GB" ...
##   ..$ 3       : chr [1:56] NA "ATL" "ARI" "GB" ...
##   ..$ 4       : chr [1:56] "BAL" "ATL" "ARI" "GB" ...
##   ..$ 5       : chr [1:56] "BAL" NA "ARI" "GB" ...
##   ..$ 6       : chr [1:56] NA "ATL" "ARI" NA ...
##   ..$ 7       : chr [1:56] "BAL" "ATL" NA NA ...
##   ..$ 8       : chr [1:56] "BAL" "ATL" NA NA ...
##   ..$ 9       : chr [1:56] "BAL" "ATL" NA NA ...
##   ..$ 10      : chr [1:56] NA "ATL" NA NA ...
##   ..$ 11      : chr [1:56] "BAL" "ATL" NA NA ...
##   ..$ 12      : chr [1:56] "BAL" "ATL" NA NA ...
##   ..$ 13      : chr [1:56] "BAL" NA NA NA ...
##   ..$ 14      : chr [1:56] "BAL" NA NA NA ...
##   ..$ 15      : chr [1:56] "BAL" "ATL" NA "GB" ...
##   ..$ 16      : chr [1:56] "BAL" "ATL" NA NA ...
##   ..$ 17      : chr [1:56] "BAL" "ATL" NA NA ...

1.3: Missing Data

1.3.1 Quarterbacks (Old Data QBdata dataset)

vis_dat(QBCrossSectional[1:30,])

vis_dat(QBCrossSectional[31:60, ])

vis_dat(QBCrossSectional[61:89, ])

1.3.2 QBCrossSectional dataset (New dataset)

vis_dat(QBCrossSectional)

print(QBCrossSectional)

## # A tibble: 453 x 21
##    PlayerID Week  Position Opponent TeamIsHome GameDate   PassingCompleti…
##       <dbl> <fct> <chr>    <chr>    <lgl>      <date>                <dbl>
##  1     6739 13    QB       NYJ      FALSE      2017-12-03               19
##  2     4314 3     QB       HOU      TRUE       2017-09-24               25
##  3    13320 10    QB       MIA      TRUE       2017-11-13               21
##  4    18857 5     QB       KC       TRUE       2017-10-08               16
##  5    14536 8     QB       HOU      TRUE       2017-10-29               26
##  6    18857 4     QB       TEN      TRUE       2017-10-01               25
##  7    14536 3     QB       TEN      FALSE      2017-09-24               29
##  8    13320 4     QB       NE       FALSE      2017-10-01               22
##  9    18857 8     QB       SEA      FALSE      2017-10-29               19
## 10    13320 15    QB       GB       TRUE       2017-12-17               20
## # … with 443 more rows, and 14 more variables: PassingAttempts <dbl>,
## #   PassingCompletionPercentage <dbl>, PassingYards <dbl>,
## #   PassingYardsPerAttempt <dbl>, PassingTouchdowns <dbl>,
## #   PassingInterceptions <dbl>, PassingRating <dbl>,
## #   RushingAttempts <dbl>, RushingYards <dbl>,
## #   RushingYardsPerAttempt <dbl>, RushingTouchdowns <dbl>,
## #   FumblesLost <dbl>, FantasyPoints <dbl>, Team <chr>

1.3.3 QBPanels dataset (New dataset)

print(QBPanels)

## $Opponent
## # A tibble: 56 x 18
##    PlayerID `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`   `10` 
##       <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
##  1      611 <NA>  CLE   <NA>  PIT   OAK   <NA>  MIN   MIA   TEN   <NA> 
##  2      732 CHI   GB    DET   BUF   <NA>  MIA   NE    NYJ   CAR   DAL  
##  3     2428 DET   IND   DAL   SF    PHI   TB    <NA>  <NA>  <NA>  <NA> 
##  4     2593 SEA   ATL   CIN   CHI   DAL   <NA>  <NA>  <NA>  <NA>  <NA> 
##  5     3807 CLE   MIN   CHI   BAL   <NA>  KC    CIN   DET   <NA>  IND  
##  6     4314 KC    NO    HOU   CAR   TB    NYJ   ATL   LAC   <NA>  DEN  
##  7     4737 <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  SF    SEA  
##  8     4932 <NA>  DET   PHI   TB    LAC   DEN   SEA   <NA>  LAR   SF   
##  9     5282 BUF   OAK   MIA   <NA>  CLE   NE    MIA   ATL   BUF   TB   
## 10     5834 <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  NYJ   <NA>  <NA>  <NA> 
## # … with 46 more rows, and 7 more variables: `11` <chr>, `12` <chr>,
## #   `13` <chr>, `14` <chr>, `15` <chr>, `16` <chr>, `17` <chr>
## 
## $TeamIsHome
## # A tibble: 56 x 18
##    PlayerID `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`   `10` 
##       <dbl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
##  1      611 NA    TRUE  NA    TRUE  FALSE NA    FALSE TRUE  FALSE NA   
##  2      732 FALSE TRUE  FALSE TRUE  NA    TRUE  FALSE FALSE FALSE TRUE 
##  3     2428 FALSE FALSE TRUE  TRUE  FALSE TRUE  NA    NA    NA    NA   
##  4     2593 TRUE  FALSE TRUE  TRUE  FALSE NA    NA    NA    NA    NA   
##  5     3807 FALSE TRUE  FALSE FALSE NA    FALSE TRUE  FALSE NA    FALSE
##  6     4314 TRUE  FALSE TRUE  TRUE  FALSE FALSE TRUE  TRUE  NA    FALSE
##  7     4737 NA    NA    NA    NA    NA    NA    NA    NA    FALSE TRUE 
##  8     4932 NA    TRUE  FALSE FALSE TRUE  FALSE TRUE  NA    TRUE  FALSE
##  9     5282 FALSE FALSE TRUE  NA    FALSE TRUE  FALSE TRUE  TRUE  FALSE
## 10     5834 NA    NA    NA    NA    NA    NA    TRUE  NA    NA    NA   
## # … with 46 more rows, and 7 more variables: `11` <lgl>, `12` <lgl>,
## #   `13` <lgl>, `14` <lgl>, `15` <lgl>, `16` <lgl>, `17` <lgl>
## 
## $PassingCompletions
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA    25    NA    31    19    NA    27    10    34    NA
##  2      732    21    19    24    24    NA    24    23    18    24    22
##  3     2428    27    19    29    33    28    18    NA    NA    NA    NA
##  4     2593    28    33    28    18    19    NA    NA    NA    NA    NA
##  5     3807    24    23    22    18    NA    17    14    17    NA    19
##  6     4314    16    30    25    32    30    20    21    32    NA    25
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA    15    24
##  8     4932    NA    22    35    30    21    11    19    NA    20    28
##  9     5282    26    16    18    NA    23    31    17    26    14    23
## 10     5834    NA    NA    NA    NA    NA    NA    13    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $PassingAttempts
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA    34    NA    49    26    NA    39    15    52    NA
##  2      732    30    28    35    42    NA    35    33    29    38    29
##  3     2428    48    36    48    51    44    22    NA    NA    NA    NA
##  4     2593    42    50    42    26    29    NA    NA    NA    NA    NA
##  5     3807    36    35    39    30    NA    25    24    31    NA    31
##  6     4314    36    39    35    45    40    38    29    47    NA    34
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA    30    47
##  8     4932    NA    32    47    49    36    19    39    NA    36    37
##  9     5282    39    24    23    NA    30    47    27    33    20    39
## 10     5834    NA    NA    NA    NA    NA    NA    21    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $PassingCompletionPercentage
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611  NA    73.5  NA    63.3  73.1  NA    69.2  66.7  65.4  NA  
##  2      732  70    67.9  68.6  57.1  NA    68.6  69.7  62.1  63.2  75.9
##  3     2428  56.2  52.8  60.4  64.7  63.6  81.8  NA    NA    NA    NA  
##  4     2593  66.7  66    66.7  69.2  65.5  NA    NA    NA    NA    NA  
##  5     3807  66.7  65.7  56.4  60    NA    68    58.3  54.8  NA    61.3
##  6     4314  44.4  76.9  71.4  71.1  75    52.6  72.4  68.1  NA    73.5
##  7     4737  NA    NA    NA    NA    NA    NA    NA    NA    50    51.1
##  8     4932  NA    68.8  74.5  61.2  58.3  57.9  48.7  NA    55.6  75.7
##  9     5282  66.7  66.7  78.3  NA    76.7  66    63    78.8  70    59  
## 10     5834  NA    NA    NA    NA    NA    NA    61.9  NA    NA    NA  
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $PassingYards
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA   217    NA   235   222    NA   186   101   261    NA
##  2      732   321   252   294   242    NA   248   233   254   313   215
##  3     2428   268   332   325   357   291   283    NA    NA    NA    NA
##  4     2593   311   343   313   179   221    NA    NA    NA    NA    NA
##  5     3807   263   243   235   216    NA   252   224   317    NA   236
##  6     4314   267   447   378   307   303   257   249   333    NA   266
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA   201   273
##  8     4932    NA   239   366   288   225   128   134    NA   220   273
##  9     5282   187   166   249    NA   194   354   209   257   140   262
## 10     5834    NA    NA    NA    NA    NA    NA   188    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $PassingYardsPerAttempt
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611  NA     6.4  NA     4.8   8.5  NA     4.8   6.7   5    NA  
##  2      732  10.7   9     8.4   5.8  NA     7.1   7.1   8.8   8.2   7.4
##  3     2428   5.6   9.2   6.8   7     6.6  12.9  NA    NA    NA    NA  
##  4     2593   7.4   6.9   7.5   6.9   7.6  NA    NA    NA    NA    NA  
##  5     3807   7.3   6.9   6     7.2  NA    10.1   9.3  10.2  NA     7.6
##  6     4314   7.4  11.5  10.8   6.8   7.6   6.8   8.6   7.1  NA     7.8
##  7     4737  NA    NA    NA    NA    NA    NA    NA    NA     6.7   5.8
##  8     4932  NA     7.5   7.8   5.9   6.2   6.7   3.4  NA     6.1   7.4
##  9     5282   4.8   6.9  10.8  NA     6.5   7.5   7.7   7.8   7     6.7
## 10     5834  NA    NA    NA    NA    NA    NA     9    NA    NA    NA  
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $PassingTouchdowns
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA     2    NA     1     0    NA     1     1     2    NA
##  2      732     1     1     2     1    NA     1     1     2     2     2
##  3     2428     1     1     2     1     1     3    NA    NA    NA    NA
##  4     2593     1     2     3     4     3    NA    NA    NA    NA    NA
##  5     3807     2     2     1     1    NA     1     2     1    NA     2
##  6     4314     0     3     5     2     1     2     2     1    NA     3
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA     2     1
##  8     4932    NA     1     3     2     2     1     1    NA     2     2
##  9     5282     0     2     1    NA     2     2     3     2     1     1
## 10     5834    NA    NA    NA    NA    NA    NA     2    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $PassingInterceptions
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA     1    NA     2     0    NA     0     0     2    NA
##  2      732     0     0     3     2    NA     1     0     0     1     1
##  3     2428     3     1     0     1     0     1    NA    NA    NA    NA
##  4     2593     1     1     1     0     0    NA    NA    NA    NA    NA
##  5     3807     1     0     0     1    NA     1     0     1    NA     1
##  6     4314     0     0     0     0     1     1     0     0    NA     0
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA     1     0
##  8     4932    NA     1     2     0     1     0     0    NA     1     0
##  9     5282     2     0     0    NA     1     2     1     0     0     1
## 10     5834    NA    NA    NA    NA    NA    NA     1    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $PassingRating
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611  NA    97.3  NA    64.6  98.6  NA    88.2 108.   74.3  NA  
##  2      732 116.  108.   77.6  61.8  NA    86.4  99.7 113.   95.6 105. 
##  3     2428  53.1  82.2  94.5  83.5  90.2 139.   NA    NA    NA    NA  
##  4     2593  86.5  90.7 103.  128.  123.   NA    NA    NA    NA    NA  
##  5     3807  95.0 105.   82.8  79.3  NA    97.4 117.   87.7  NA    92.9
##  6     4314  70.0 140.  146.  105.   94.1  80.7 121.   95.4  NA   125. 
##  7     4737  NA    NA    NA    NA    NA    NA    NA    NA    80    75.9
##  8     4932  NA    87.9 100.   91.2  83.7  95.9  65.5  NA    80.8 114. 
##  9     5282  56.2 114.  126.   NA   101.   84.9 108.  119.  106.   77.1
## 10     5834  NA    NA    NA    NA    NA    NA   103.   NA    NA    NA  
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $RushingAttempts
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA     1    NA     0     3    NA     1     1     1    NA
##  2      732     3     1     1     1    NA     0     3     6     1     0
##  3     2428     1     3     3     2     0     5    NA    NA    NA    NA
##  4     2593     7     2     4     1     4    NA    NA    NA    NA    NA
##  5     3807     3     2     1     1    NA     2     3     3    NA     2
##  6     4314     2     2     1     1     2     1     5     1    NA     1
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA     4     1
##  8     4932    NA     0     0     3     0     1     0    NA     0     0
##  9     5282     2     4     4    NA     3     3     3     1     5     0
## 10     5834    NA    NA    NA    NA    NA    NA     0    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $RushingYards
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA    -1    NA     0    -4    NA     0     9     7    NA
##  2      732    11     8    -1     8    NA     0    37    17    -1     0
##  3     2428     2     6     7     1     0    -4    NA    NA    NA    NA
##  4     2593    21     8    23    -1    32    NA    NA    NA    NA    NA
##  5     3807    -8    -2     6    -1    NA    -2    -3    -2    NA     5
##  6     4314     0     9     6     2     5    -1     5     2    NA     0
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA     2     1
##  8     4932    NA     0     0    22     0     0     0    NA     0     0
##  9     5282     0    31     7    NA    -3    21     1     0    13     0
## 10     5834    NA    NA    NA    NA    NA    NA     0    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $RushingYardsPerAttempt
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611  NA    -1    NA     0    -1.3  NA     0     9     7    NA  
##  2      732   3.7   8    -1     8    NA     0    12.3   2.8  -1     0  
##  3     2428   2     2     2.3   0.5   0    -0.8  NA    NA    NA    NA  
##  4     2593   3     4     5.8  -1     8    NA    NA    NA    NA    NA  
##  5     3807  -2.7  -1     6    -1    NA    -1    -1    -0.7  NA     2.5
##  6     4314   0     4.5   6     2     2.5  -1     1     2    NA     0  
##  7     4737  NA    NA    NA    NA    NA    NA    NA    NA     0.5   1  
##  8     4932  NA     0     0     7.3   0     0     0    NA     0     0  
##  9     5282   0     7.8   1.8  NA    -1     7     0.3   0     2.6   0  
## 10     5834  NA    NA    NA    NA    NA    NA     0    NA    NA    NA  
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $RushingTouchdowns
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA     0    NA     0     0    NA     0     0     0    NA
##  2      732     0     0     0     0    NA     0     0     0     0     0
##  3     2428     0     0     0     0     0     0    NA    NA    NA    NA
##  4     2593     0     0     0     0     0    NA    NA    NA    NA    NA
##  5     3807     0     0     0     0    NA     0     0     0    NA     0
##  6     4314     0     0     0     0     0     0     0     0    NA     0
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA     0     0
##  8     4932    NA     0     0     1     0     0     0    NA     0     0
##  9     5282     1     0     0    NA     0     0     1     0     1     0
## 10     5834    NA    NA    NA    NA    NA    NA     0    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $FumblesLost
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611    NA     0    NA     0     0    NA     0     0     0    NA
##  2      732     0     0     0     1    NA     0     0     2     0     0
##  3     2428     0     0     0     0     0     0    NA    NA    NA    NA
##  4     2593     0     1     0     0     0    NA    NA    NA    NA    NA
##  5     3807     0     0     1     0    NA     0     0     0    NA     0
##  6     4314     0     0     1     0     1     0     0     0    NA     0
##  7     4737    NA    NA    NA    NA    NA    NA    NA    NA     0     0
##  8     4932    NA     0     0     0     1     0     1    NA     1     1
##  9     5282     0     1     0    NA     0     0     0     0     0     0
## 10     5834    NA    NA    NA    NA    NA    NA     0    NA    NA    NA
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $FantasyPoints
## # A tibble: 56 x 18
##    PlayerID   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`
##       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1      611 NA     14.6  NA    9.4   8.48 NA    11.4   8.94  15.1  NA  
##  2      732 17.9   14.9  13.7  8.48 NA    11.9  17.0  15.9   18.4  14.6
##  3     2428  8.92  15.9  21.7 16.4  15.6  20.9  NA    NA     NA    NA  
##  4     2593 16.5   18.5  24.8 23.1  24.0  NA    NA    NA     NA    NA  
##  5     3807 15.7   17.5  12   10.5  NA    11.9  16.7  14.5   NA    17.9
##  6     4314 10.7   30.8  35.7 20.5  12.6  16.2  18.5  17.5   NA    22.6
##  7     4737 NA     NA    NA   NA    NA    NA    NA    NA     14.2  15.0
##  8     4932 NA     11.6  22.6 27.7  13     9.12  7.36 NA     12.8  16.9
##  9     5282  9.48  15.7  14.7 NA    13.5  20.3  24.5  18.3   16.9  12.5
## 10     5834 NA     NA    NA   NA    NA    NA    13.5  NA     NA    NA  
## # … with 46 more rows, and 7 more variables: `11` <dbl>, `12` <dbl>,
## #   `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>
## 
## $Team
## # A tibble: 56 x 18
##    PlayerID `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`   `10` 
##       <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
##  1      611 <NA>  BAL   <NA>  BAL   BAL   <NA>  BAL   BAL   BAL   <NA> 
##  2      732 ATL   ATL   ATL   ATL   <NA>  ATL   ATL   ATL   ATL   ATL  
##  3     2428 ARI   ARI   ARI   ARI   ARI   ARI   <NA>  <NA>  <NA>  <NA> 
##  4     2593 GB    GB    GB    GB    GB    <NA>  <NA>  <NA>  <NA>  <NA> 
##  5     3807 PIT   PIT   PIT   PIT   <NA>  PIT   PIT   PIT   <NA>  PIT  
##  6     4314 NE    NE    NE    NE    NE    NE    NE    NE    <NA>  NE   
##  7     4737 <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  ARI   ARI  
##  8     4932 <NA>  NYG   NYG   NYG   NYG   NYG   NYG   <NA>  NYG   NYG  
##  9     5282 NYJ   NYJ   NYJ   <NA>  NYJ   NYJ   NYJ   NYJ   NYJ   NYJ  
## 10     5834 <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  MIA   <NA>  <NA>  <NA> 
## # … with 46 more rows, and 7 more variables: `11` <chr>, `12` <chr>,
## #   `13` <chr>, `14` <chr>, `15` <chr>, `16` <chr>, `17` <chr>

for (panel in 1:length(QBPanels)) {
   missing_plot <- vis_miss(as.data.frame(QBPanels[panel]))
   print(missing_plot)
}

1.4: Corrrelogram

1.4.1 Corrrelogram Style1 QBData (Old dataset)

#QBdata_num <- QBCrossSectional[-c(1:3,5:16,29:36,39:52,55:56,74:89)]
#corr <- round(cor(QBdata_num), 1)

#ggcorrplot(corr, hc.order = TRUE, 
#           type = "full", 
#           lab = TRUE, 
#           lab_size = 1.5, 
#           method="square", 
#           colors = c("tomato2", "white", "springgreen3"), 
#           title="Correlogram of Quarterbacks", tl.cex = 7,pch=2,pch.col =3,show.diag = T,
#           ggtheme=theme_classic)

1.4.2 Corrrelogram Style2 QBData (Old dataset)

#corrplot(corr, method="circle",tl.cex = 0.55,tl.col = "#1C1C1C")

1.4.3 Corrrelogram of Crosssectional data (New dataset)

QBX_ds <- QBCrossSectional[-c(1:6,21)]
corr <- round(cor(QBX_ds), 1)
ggcorrplot(corr, hc.order = TRUE, 
           type = "full", 
           lab = TRUE, 
           lab_size = 1.5, 
           method="square", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of Quarterbacks", tl.cex = 7,pch=2,pch.col =3,show.diag = T,
           ggtheme=theme_classic)

1.5: Distributions

1.5.1 Density plot for Fantasypoints is approxmiately Normal (Old QBData)

library(e1071)
par(mfrow=c(1, 2))  # divide graph area in 2 columns
target <- QBCrossSectional$FantasyPoints
plot(density(target), main="QBdata: FantasyPoints", ylab="Frequency", sub=paste("Skewness:", round(e1071::skewness(target), 2)))
polygon(density(target), col="red",asp=1.2 )

1.5.2 Density plot for Fantasypoints is approxmiately Normal (New QBCrossSectional)

par(mfrow=c(1, 2))  # divide graph area in 2 columns
target <- QBCrossSectional$FantasyPoints
plot(density(target), main="CrossSectional Dataset: FantasyPoints", ylab="Frequency", sub=paste("Skewness:", round(e1071::skewness(target), 2)))
polygon(density(target), col="red")

1.5.3 Boxplots - Target and Individual Predictor Bevavior for per Team

I think we should do these by week - not team - we want to look at variance of the time series across obervations

QBCrossSectional %>% ggplot(aes(y=FantasyPoints,x=Week,fill=Week,group=Week))+
                     geom_boxplot(show.legend = FALSE)+
                     xlab("Week")+ylab("FantasyPoints")+
                     labs(title="Fantasyfootball", 
                     subtitle="Weekly fantasypoints", 
                     aption="Source: Fantasyfootball")+theme_economist()

for (i in 7:19) {

ggplotp <- QBCrossSectional %>% ggplot(aes_string(y=names(QBCrossSectional[i]),x=Week,fill=Week,group=Week))+
                     geom_boxplot(show.legend = FALSE)+
                     xlab("Week")+ylab(names(QBCrossSectional[i]))+
                     labs(title="Fantasyfootball", 
                     subtitle="Weekly fantasypoints", 
                     aption="Source: Fantasyfootball")+theme_economist()  
  print(ggplotp)
}

Check for skewed predictors

1.5.4 Histograms - Normality by Week

features_to_keep = c('PassingYards', 'PassingAttempts', 'PassingTouchdowns', 'PassingCompletions', 'PassingYardsPerAttempt')
for(f in features_to_keep){
  hist = QBCrossSectional %>% ggplot(aes_string(x=f))+
                              geom_histogram(bins=30,aes(fill=Week),show.legend = FALSE)+
                              facet_wrap(~Week,ncol=8)+theme_economist()+theme(axis.text.x = element_text(angle = 45, hjust = 1))
                              
  print(hist)
}

1.6: Relationships

1.6.1 Relationship between FanatasyPoints with all predictors and taking Opponent into consideration

Not sure that combining these is useful

attach(QBCrossSectional)

## The following objects are masked from QBCrossSectional (pos = 4):
## 
##     FantasyPoints, FumblesLost, GameDate, Opponent,
##     PassingAttempts, PassingCompletionPercentage,
##     PassingCompletions, PassingInterceptions, PassingRating,
##     PassingTouchdowns, PassingYards, PassingYardsPerAttempt,
##     PlayerID, Position, RushingAttempts, RushingTouchdowns,
##     RushingYards, RushingYardsPerAttempt, Team, TeamIsHome, Week

## The following object is masked from package:ggplot2:
## 
##     Position

QBCrossSectional %>% ggplot(aes(y=FantasyPoints,x=PassingCompletions+PassingAttempts+
                                                  PassingCompletionPercentage+
                                                  PassingYards+PassingYardsPerAttempt+
                                                  PassingTouchdowns+PassingInterceptions+
                                                  PassingRating+RushingAttempts+RushingYards+
                                                  RushingYardsPerAttempt+RushingTouchdowns+FumblesLost,
                                                  color=Opponent))+ 
                     xlab("Relationship of predictors with each Opponent")+
                     geom_point()+
                     geom_smooth(method="lm",se=F)+
                     theme(axis.text.x = element_text(angle=65, vjust=0.6))+facet_wrap(~Opponent)

1.6.2 Relationship between FanatasyPoints with all predictors and taking Home turf into consideration

Same here…..

attach(QBCrossSectional)

## The following objects are masked from QBCrossSectional (pos = 3):
## 
##     FantasyPoints, FumblesLost, GameDate, Opponent,
##     PassingAttempts, PassingCompletionPercentage,
##     PassingCompletions, PassingInterceptions, PassingRating,
##     PassingTouchdowns, PassingYards, PassingYardsPerAttempt,
##     PlayerID, Position, RushingAttempts, RushingTouchdowns,
##     RushingYards, RushingYardsPerAttempt, Team, TeamIsHome, Week

## The following objects are masked from QBCrossSectional (pos = 5):
## 
##     FantasyPoints, FumblesLost, GameDate, Opponent,
##     PassingAttempts, PassingCompletionPercentage,
##     PassingCompletions, PassingInterceptions, PassingRating,
##     PassingTouchdowns, PassingYards, PassingYardsPerAttempt,
##     PlayerID, Position, RushingAttempts, RushingTouchdowns,
##     RushingYards, RushingYardsPerAttempt, Team, TeamIsHome, Week

## The following object is masked from package:ggplot2:
## 
##     Position

QBCrossSectional %>% ggplot(aes(y=FantasyPoints,x=PassingCompletions+PassingAttempts+
                                                  PassingCompletionPercentage+
                                                  PassingYards+PassingYardsPerAttempt+
                                                  PassingTouchdowns+PassingInterceptions+
                                                  PassingRating+RushingAttempts+RushingYards+
                                                  RushingYardsPerAttempt+RushingTouchdowns+FumblesLost,
                                                  color=TeamIsHome))+
                     geom_point()+
                     geom_smooth(method="lm",se=F)+
                     theme(axis.text.x = element_text(angle=65, vjust=0.6))+facet_wrap(~TeamIsHome)

1.6.3 Relationship between FanatasyPoints with all predictors per team

and here……

attach(QBCrossSectional)

## The following objects are masked from QBCrossSectional (pos = 3):
## 
##     FantasyPoints, FumblesLost, GameDate, Opponent,
##     PassingAttempts, PassingCompletionPercentage,
##     PassingCompletions, PassingInterceptions, PassingRating,
##     PassingTouchdowns, PassingYards, PassingYardsPerAttempt,
##     PlayerID, Position, RushingAttempts, RushingTouchdowns,
##     RushingYards, RushingYardsPerAttempt, Team, TeamIsHome, Week

## The following objects are masked from QBCrossSectional (pos = 4):
## 
##     FantasyPoints, FumblesLost, GameDate, Opponent,
##     PassingAttempts, PassingCompletionPercentage,
##     PassingCompletions, PassingInterceptions, PassingRating,
##     PassingTouchdowns, PassingYards, PassingYardsPerAttempt,
##     PlayerID, Position, RushingAttempts, RushingTouchdowns,
##     RushingYards, RushingYardsPerAttempt, Team, TeamIsHome, Week

## The following objects are masked from QBCrossSectional (pos = 6):
## 
##     FantasyPoints, FumblesLost, GameDate, Opponent,
##     PassingAttempts, PassingCompletionPercentage,
##     PassingCompletions, PassingInterceptions, PassingRating,
##     PassingTouchdowns, PassingYards, PassingYardsPerAttempt,
##     PlayerID, Position, RushingAttempts, RushingTouchdowns,
##     RushingYards, RushingYardsPerAttempt, Team, TeamIsHome, Week

## The following object is masked from package:ggplot2:
## 
##     Position

QBCrossSectional %>% ggplot(aes(y=FantasyPoints,x=PassingCompletions+PassingAttempts+
                                                  PassingCompletionPercentage+
                                                  PassingYards+PassingYardsPerAttempt+
                                                  PassingTouchdowns+PassingInterceptions+
                                                  PassingRating+RushingAttempts+
                                                  RushingYards+RushingYardsPerAttempt+
                                                  RushingTouchdowns+FumblesLost,color=Team)) +
                     geom_point()+
                     geom_smooth(method="lm",se=F)+
                     theme(axis.text.x = element_text(angle=65, vjust=0.6))+facet_wrap(~Team)

1.6.4 Relationship between FantasyPoints and Individual predictors

This makes sense - but we are actually trying to capture what the NEXT value for fantasy points is likely to be - these are all going to be highly correlated to the current 
week since the fantasy score is a linear combination of the predictors for any given week. We need to shift the data (and z-score it?) before running these charts.

for (i in 7:19) {
  linear_plot <- QBCrossSectional %>% 
                 ggplot(aes_string(y="FantasyPoints",x=names(QBCrossSectional[i]))) + 
                 geom_point()+geom_smooth(method="lm",se=F)+
                 theme(axis.text.x = element_text(angle=65, vjust=0.6))+
                 theme_economist()
    print(linear_plot) 
  }

Drop rushing yards per attempt

Defensive stats

Player Defensive Stats

defensive_positions = getFootballData("https://fantasydata.com/FantasyStatsNFL/FantasyStats_Read?sort=FantasyPoints-desc&pageSize=4000&group=&filter=&filters.position=8&filters.team=&filters.teamkey=&filters.season=2017&filters.seasontype=1&filters.scope=2&filters.subscope=1&filters.redzonescope=&filters.scoringsystem=&filters.leaguetype=&filters.searchtext=&filters.week=&filters.startweek=1&filters.endweek=17&filters.minimumsnaps=&filters.teamaspect=&filters.stattype=&filters.exportType=&filters.desktop=&filter#s.dfsoperator=&filters.dfsslateid=&filters.dfsslategameid=&filters.dfsrosterslot=&filters.page=&filters.showfavs=&filters.posgroup=&filters.oddsstate=&filters.aggregatesc#ope=1&filters.rangescope=&filters.range=1")

defensive_positions$StatSummary = c(NULL)

Team Defensive Stats

team_defense = getFootballData("https://fantasydata.com/FantasyStatsNFL/FantasyStats_Read?sort=FantasyPoints-desc&pageSize=4000&group=&filter=&filters.position=7&filters.team=&filters.teamkey=&filters.season=2017&filters.seasontype=1&filters.scope=2&filters.subscope=1&filters.redzonescope=&filters.scoringsystem=&filters.leaguetype=&filters.searchtext=&filters.week=&filters.startweek=1&filters.endweek=17&filters.minimumsnaps=&filters.teamaspect=&filters.stattype=&filters.exportType=&filters.desktop=&filters.dfsoperator=&filters.dfsslateid=&filters.dfsslategameid=&filters.dfsrosterslot=&filters.page=&filters.showfavs=&filters.posgroup=&filters.oddsstate=&filters.aggregatescope=1&filters.rangescope=&filters.range=1")
team_defense$StatSummary = c(NULL)

defensive_players_by_week =  sqldf("SELECT 
                                      Week, 
                                      FantasyPosition,
                                      Count(*)
                                    FROM defensive_positions
                                    GROUP BY Week, FantasyPosition")

team_defense_by_week =  sqldf("SELECT 
                                      Week, 
                                      FantasyPosition,
                                      Count(*)
                                    FROM team_defense
                                    GROUP BY Week, FantasyPosition")

# this looks good some teams are on bye in certain weeks

Do some averages for Defense before stitching on to QB data

defensive_columns = c('Team', 'Week', 'TacklesForLoss', 'Sacks', 'QuarterbackHits', 'Interceptions', 'FumblesRecovered', 'Safeties', 'DefensiveTouchdowns', 'SoloTackles', 'AssistedTackles', 'SackYards', 'PassesDefended', 'FumblesForced', 'FantasyPoints', 'PointsAllowedByDefenseSpecialTeams')


team_defense = team_defense %>% select(defensive_columns) %>% rename('DefensiveFantasyPoints'='FantasyPoints') %>% group_by(Week) %>% mutate(WeeklyRank = dense_rank(DefensiveFantasyPoints)) 
attach(team_defense)

## The following objects are masked from QBCrossSectional (pos = 3):
## 
##     Team, Week

## The following objects are masked from QBCrossSectional (pos = 4):
## 
##     Team, Week

## The following objects are masked from QBCrossSectional (pos = 5):
## 
##     Team, Week

## The following objects are masked from QBCrossSectional (pos = 7):
## 
##     Team, Week

team_defense$Week = as.factor(team_defense$Week)  
  
sqldf("SELECT Week, Team, WeeklyRank FROM team_defense  WHERE Week IN(1, 2) ORDER BY Team, Week")

##    Week Team WeeklyRank
## 1     1  ARI          8
## 2     2  ARI         12
## 3     1  ATL          6
## 4     2  ATL         14
## 5     1  BAL         14
## 6     2  BAL         15
## 7     1  BUF          9
## 8     2  BUF         12
## 9     1  CAR         11
## 10    2  CAR         12
## 11    1  CHI          3
## 12    2  CHI          6
## 13    1  CIN          5
## 14    2  CIN          9
## 15    1  CLE          4
## 16    2  CLE          8
## 17    1  DAL         10
## 18    2  DAL          4
## 19    1  DEN          4
## 20    2  DEN         14
## 21    1  DET         12
## 22    2  DET         15
## 23    1   GB          9
## 24    2   GB          4
## 25    1  HOU          1
## 26    2  HOU         11
## 27    1  IND          3
## 28    2  IND          9
## 29    1  JAX         15
## 30    2  JAX          3
## 31    1   KC          4
## 32    2   KC         13
## 33    1  LAC          8
## 34    2  LAC          5
## 35    1  LAR         15
## 36    2  LAR          4
## 37    2  MIA          4
## 38    1  MIN          3
## 39    2  MIN          4
## 40    1   NE          2
## 41    2   NE          4
## 42    1   NO          1
## 43    2   NO          2
## 44    1  NYG          3
## 45    2  NYG          7
## 46    1  NYJ          5
## 47    2  NYJ          1
## 48    1  OAK          3
## 49    2  OAK         11
## 50    1  PHI         13
## 51    2  PHI          6
## 52    1  PIT         12
## 53    2  PIT         10
## 54    1  SEA          7
## 55    2  SEA         10
## 56    1   SF          5
## 57    2   SF          9
## 58    2   TB         16
## 59    1  TEN          3
## 60    2  TEN         11
## 61    1  WAS         10
## 62    2  WAS          9

Add defensive matchups

QBCrossSectionalDefensiveOverlay = QBCrossSectional %>% left_join(team_defense, by = c('Week'='Week', 'Opponent'='Team' )) 

Add some lag data for QB

CumulativeVariables (these should definitley be combined into a weekly ranking)

#Should we just train on second half of 2017?
QBCrossSectionalDefensiveOverlayCumulativePassYards = QBCrossSectionalDefensiveOverlay %>% group_by(PlayerID) %>% arrange(Week) %>%           mutate(CumulativeAveragePassingYards=cummean(PassingYards)
        , CumulativeAveragePassingTouchdowns=cummean(PassingTouchdowns)
        , CumulativeAverageCompletions = cummean(PassingCompletions) # not sure that completions matter much - most leagues don't reward them
        , CumulativeMaxPassingTouchdowns = cummax(PassingTouchdowns) 
        , CumulativeMaxPassingYards = cummax(PassingYards)
        , CumulativeMaxPassingAttempts = cummax(PassingAttempts)
        , CumulativeMaxPassingRating = cummax(PassingRating)
        , CumulativeMaxCompletions = cummax(PassingCompletions)
        , CumulativeMaxPassYardsPerAttempt = cummax(PassingYardsPerAttempt)
        , CumulativeMinPassingTouchdowns = cummin(PassingTouchdowns) 
        , CumulativeMinPassingYards = cummin(PassingYards)   #Let's get mins to capture downside risk
        , CumulativeMinPassingAttempts = cummin(PassingAttempts)
        , CumulativeMinPassingRating = cummin(PassingRating)
        , CumulativeMinCompletions = cummin(PassingCompletions)
        , CumulativeMinPassYardsPerAttempt = cummin(PassingYardsPerAttempt)
        , LastWeekQuarterBackRating = lag(PassingRating)
        , LastWeekQuarterPassingYards = lag(PassingYards)
        , LastWeekQuarterPassingTouchdowns = lag(PassingTouchdowns)
        , NextWeekFantasyPoints = lead(FantasyPoints) #Target Variable
        )

QBCrossSectionalDefensiveOverlayCumulativePassYards %>% filter(PlayerID == 6739) %>% write.csv('alex_smith.csv') #Alex Smith did indeed pass for 4042 yards on the season:)

Do we want to convert any of these to factors?